# Safety Outdoor Navigation Egocentric (SONE) Dataset
Version 1.0 — December 2025  
Author: Edgar Guzman, Harvard University  

---

## 1. Overview
The Safety Outdoor Navigation Egocentric (SONE) Dataset consists of RGB image sequences captured from an egocentric (first-person) camera during outdoor sidewalk navigation. The dataset was created to support research in safe mobility, hazard detection, anomaly detection, and assistive navigation technologies.

The dataset includes:
- **Normal navigation scenes** for VAE and SVM training
- **Hazardous and non-hazardous anomaly scenes** for evaluation  

This dataset enables reconstruction-based anomaly detection and robust hazard classification for uncertain outdoor environments.

---

## 2. Contents
When downloaded from Harvard Dataverse, the dataset is bundled as dataverse_files.zip.
This bundle contains the following two archives:

VAE.zip — raw RGB images for Variational Autoencoder (VAE) training and testing

SVM.zip — Images used for One-Class SVM (OCSVM) training, validation, and testing

After extracting dataverse_files.zip, you will see the following files:

```
VAE_Training/
    Train/
        cement/
        brick/
        ...
    Test/
        cement/
            pothole/
            uneven/
            dirt/
            ...
        brick/
            ice/
            anom/
            ...
        ...

SVM_Training/
    Train/
    Validation/
    Test/
```

Each folder contains sequential RGB frames (`.png`).

---

## 3. Data Characteristics
- **Modality:** RGB  
- **Viewpoint:** Egocentric (first-person)  
- **Environment:** Real outdoor sidewalks  
- **Resolution:** 640×480 pixels  
- **VAE training set:** >20,000 images (normal scenes)  
- **VAE testing set:** ~8,000 images (hazardous & non-hazardous anomalies)  
- **SVM training:** Images for trianing and testing

---

## 4. Intended Use
The SONE dataset is intended for:

- Reconstruction-based anomaly detection (VAE, AE, GAN, etc.)  
- One-class classification (OCSVM, Deep SVDD, Multi-Sphere SVDD)  
- Hazard detection for outdoor navigation  
- Assistive mobility applications  
- Benchmarking computer vision robustness  

---

## 5. How to Extract the Dataset
After downloading from Dataverse:

### macOS or Linux:
```bash
unzip VAE.zip
unzip SVM.zip
```

### Windows:
Right-click → **Extract All…**

The extracted folder will match the directory structure shown above.

---

## 6. Citation
If you use this dataset, please cite:

**Edgar Guzman (2025). Safety Outdoor Navigation Egocentric (SONE) Dataset.  
Harvard Dataverse. https://doi.org/10.7910/DVN/ZLYKI9**

BibTeX:

```bibtex
@dataset{guzman2025sone,
  author       = {Guzman, Edgar},
  title        = {Safety Outdoor Navigation Egocentric (SONE) Dataset},
  year         = {2025},
  publisher    = {Harvard Dataverse},
  doi          = {10.7910/DVN/ZLYKI9}
}
```

---

## 7. License
This dataset is released under the **CC0 1.0 Public Domain Dedication**.  
Users may copy, modify, distribute, and use the dataset for any purpose without restriction.

---

## 8. Contact
For questions or additional information:

**Edgar Guzman**  
Harvard University  
Email: eguzman@g.harvard.edu  

---
